Objective To study the comparability of liver function results between the two detection systems. Methods Based on the NCCLS EP9-A document, 8 fresh serum samples were collected daily for the assay of 10 routine liver function parameters by utilizing the Olympus AU1000 and Backman CX7 detection systems respectively. The results were recorded over 5 days consecutively. Linearity equation and relative deviations were calculated. The comparability between the results obtained from different detection systems was evaluated according to the systematic error at medical decision level of CLIA, 88. Results The paired t-test showed that the results of the fresh serum samples had no significant difference between the two detection systems (Pgt;0.05). Except that the systematic error of albumin at low medical decision level exceeded the allowable error, all the other systematic errors at medical decision level were acceptable. Conclusion The results of liver function are comparable between the two detection systems, and the systematic errors between the two detection systems are clinically acceptable.
Heart rate variability (HRV) analysis technology based on an autoregressive (AR) model is widely used in the assessment of autonomic nervous system function. The order of AR models has important influence on the accuracy of HRV analysis. This article presents a method to determine the optimum order of AR models. After acquiring the ECG signal of 46 healthy adults in their natural breathing state and extracting the beat-to-beat intervals (RRI) in the ECG, we used two criteria, i.e. final prediction error (FPE ) criterion to estimate the optimum model order for AR models, and prediction error whiteness test to decide the reliability of the model. We compared the frequency domain parameters including total power, power in high frequency (HF), power in low frequency (LF), LF power in normalized units and ratio of LF/HF of our HRV analysis to the results of Kubios-HRV. The results showed that the correlation coefficients of the five parameters between our methods and Kubios-HRV were greater than 0.95, and the Bland-Altman plot of the parameters was in the consistent band. The results indicate that the optimization algorithm of HRV analysis based on AR models proposed in this paper can obtain accurate results, and the results of this algorithm has good coherence with those of the Kubios-HRV software in HRV analysis.
Objective To investigate the value of back propagation (BP) neural network for recognizing gastric cancer cell. Methods A total of 510 cells was selected from 308 patients. There were 210 gastric adenocarcinoma cells and 300 non-cancer gastric cells. Ten morphological parameters were measured for each cell. These data were randomly divided into two groups: training dataset (A) and test dataset (B). A three-layer BP neural network was built and trained by using dataset A. The network was then tested with dataset A and B.Results For data A, the sensitivity of network was 99%, specificity 99%, positive predictive value 98%, negative predictive value 99%, and accuracy 99%. For data B, the sensitivity of network was 99%, specificity 97%, positive predictive value 96%, negative predictive value 99%, the accuracy 98%. With receiver operator characteristic (ROC) curve evaluation, the area under ROC curve was 0.99.Conclusion The model based on BP neural network is very effective. A BP neural network can be used for effectively recognizing gastric cancer cell.
The COSMIN community updated the COSMIN-RoB checklist on reliability and measurement error in 2021. The updated checklist can be applied to the assessment of all types of outcome measurement studies, including clinician-reported outcome measures (ClinPOMs), performance-basd outcome measurement instruments (PerFOMs), and laboratory values. In order to help readers better understand and apply the updated COSMIN-RoB checklist and provide methodological references for conducting systematic reviews of ClinPOMs, PerFOMs and laboratory values, this paper aimed to interpret the updated COSMIN-RoB checklist on reliability and measurement error studies.
【摘要】 目的 分析护理基本技能考核评分误差产生的原因。 方法 使用单因素方差分析法对我校高中起点三年制护理专业2006级440名学生及2007年某校护理专业高中起点三年制护理专业2006级281名学生的护理基本技能考核成绩进行分析，并从2006级440名学生中随机抽取129名学生利用自制问卷进行调查。 结果 考核项目和监考教师不同是造成学生护理基本技能考核成绩评分存在误差的主要原因。 结论 监考教师应统一并细化考核标准及制定护理基本技能考核项目难度系数。【Abstract】 Objective To analyze the reasons of grading error in nursing basic technique examination. Methods The results of nursing basic technique examination of 440 three-year-college students in grade 2006 majored in nursing in Cangzhou Medical College and 281 three-year-college students in grade 2007 majored in nursing in Hebei Provincial Medical College were retrospectively analyzed by one-factor analysis of variance, and 129 students who were selected randomly from the students in grade 2006 were investigated via a self-made questionnaire. Results The reasons of grading error were different assessment items and different invigilators. Conclusion Unifying the difficulty level and the standard of the assessment may decrease the grading errors in nursing basic technique examination.
This is the fifth paper in the evidence-based medicine glossary series. In this paper, we mainly introduce the systematic error（bias）and random error in medical research, review the definition and classification of bias made by different institutions and individuals. We also identify and categorize more than 10 types of bias in systematic review, which are considered the best evidence in evidence-based medicine. Additionally, we introduce some methods to reduce and eliminate bias, explaine six subject headings related to bias in MeSH, and introduce a new glossary — uncertainty in metrology.
To investigate the γ pass rate limit of plan verification equipment for volumetric modulated arc therapy (VMAT) plan verification and its sensitivity on the opening and closing errors of multi-leaf collimator (MLC), 50 cases of nasopharyngeal carcinoma VMAT plan with clockwise and counterclockwise full arcs were randomly selected. Eight kinds of MLC opening and closing errors were introduced in 10 cases of them, and 80 plans with errors were generated. Firstly, the plan verification was conducted in the form of field-by-field measurement and true composite measurement. The γ analysis with the criteria of 3% dose difference, distance to agreement of 2 mm, 10% dose threshold, and absolute dose global normalized conditions were performed for these fields. Then gradient analysis was used to investigate the sensitivity of field-by-field measurement and true composite measurement on MLC opening and closing errors, and the receiver operating characteristic curve (ROC) was used to investigate the optimal threshold of γ pass rate for identifying errors. Tolerance limits and action limits for γ pass rates were calculated using statistical process control (SPC) method for another 40 cases. The error identification ability using the tolerance limit calculated by SPC method and the universal tolerance limit (95%) were compared with using the optimal threshold of ROC. The results show that for the true composite measurement, the clockwise arc and the counterclockwise arc, the descent gradients of the γ passing rate with per millimeter MLC opening error are 10.61%, 7.62% and 6.66%, respectively, and the descent gradients with per millimeter MLC closing error are 9.75%, 7.36% and 6.37%, respectively. The optimal thresholds obtained by the ROC method are 99.35%, 97.95% and 98.25%, respectively, and the tolerance limits obtained by the SPC method are 98.98%, 97.74% and 98.62%, respectively. The tolerance limit calculated by SPC method is close to the optimal threshold of ROC, both of which could identify all errors of ±2 mm, while the universal tolerance limit can only partially identify them, indicating that the universal tolerance limit is not sensitive on some large errors. Therefore, considering the factors such as ease of use and accuracy, it is suggested to use the true composite measurement in clinical practice, and to formulate tolerance limits and action limits suitable for the actual process of the institution based on the SPC method. In conclusion, it is expected that the results of this study can provide some references for institutions to optimize the radiotherapy plan verification process, set appropriate pass rate limit, and promote the standardization of plan verification.
As a valid method in systematic review, dose-response meta-analysis is widely used in investigating the relationship between independent variable and dependent variable, and which usually based on observational studies. With large sample size, observational studies can provide a reasonable amount of statistical power for meta-analysis. However, due to the design defects of observational studies, they tend to introduce many kinds of biases, which may influence the final results that make them deviation from the truth. Given the dead zone of methodology, there is no any bias adjusting method in dose-response meta-analysis. In this article, we will introduce some bias adjusting methods from other observational-study-based meta-analysis and make them suit for dose-response meta-analysis, and then compare the advantages and disadvantages of these methods.
Because of the long acquisition time and spin-echo planar imaging sequence, diffusion weight magnetic resonance image (DWI) should be denoised effectively to ensure the follow-up applications. The commonly used denoising methods which induced from gray level image lack the use of the specific information from multiple magnitude directions. This paper, therefore, proposes a modified linear minimum mean square error (LMMSE) denosing method used for DWI. The proposed method uses the local information to estimate the parameter of the Rician noise and modifies the LMMSE using the information of multiple magnitude directions synthetically. The simulation and experiment of the synthetic DWI and real human brain DWI dataset demonstrate that the proposed method can more effectively remove the Rician noise compared to the commonly used denoising method and improve the robustness and validity of the diffusion tensor magnetic resonance image (DTI).